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Results for “Quantitative Trait Loci”

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PubMed2003

Fine linkage mapping enables dissection of closely linked quantitative trait loci for seed dormancy and heading in rice.

Two quantitative trait loci (QTLs) for seed dormancy (tentatively designated Sdr1) and heading date ( Hd8) have been mapped to approximately the same region on chromosome 3 by interval mapping of backcross inbred lines derived from crosses between the rice cultivars Nipponbare (japonica) and Kasalath (indica). To clarify whether Sdr1 and Hd8 could be dissected genetically, we carried out fine-scale mapping with an advanced backcross progeny. We selected a BC(4)F(1) plant, in which a small chromosomal region including Sdr1 and Hd8, on the short arm of chromosome 3, remained heterozygous, whereas all the other chromosomal regions were homozygous for Nipponbare. Days-to-heading and seed germination rate in the BC(4)F(2) plants showed continuous variation. Ten BC(4)F(2) plants with recombination in the vicinity of Sdr1 and Hd8 were selected on the basis of the genotypes of the restriction fragment length polymorphism (RFLP) markers flanking both QTLs. Genotypes of those plants for Sdr1 and Hd8 were determined by advanced progeny testing of BC(4)F(4) families. Sdr1 was mapped between the RFLP markers R10942 and C2045, and co-segregated with C1488. Hd8 was also mapped between C12534S and R10942. Six recombination events were detected between Sdr1 and Hd8. These results clearly demonstrate that Sdr1 and Hd8 were tightly linked. Nearly isogenic lines for Sdr1 and Hd8 were selected by marker-assisted selection.

Chromosome Mapping↗

PubMed2004

Identification of quantitative trait loci for growth and carcass composition in cattle.

A genomic screening to detect quantitative trait loci (QTL) affecting growth, carcass composition and meat quality traits was pursued. Two hundred nineteen microsatellite markers were genotyped on 176 of 620 (28%) progeny from a Brahman x Angus sire mated to mostly MARC III dams. Selective genotyping, based on retail product yield (%) and fat yield (%), was used to select individuals to be genotyped. Traits included in the study were birth weight (kg), hot carcass weight (kg), retail product yield, fat yield, marbling score (400 = slight00 and 500 = small00), USDA yield grade, and estimated kidney, heart and pelvic fat (%). The QTL were classified as significant when the expected number of false positives (ENFP) was less than 0.05 (F-statistic greater than 17.3), and suggestive when the ENFP was <1 (F-statistic between 10.2 and 17.3). A significant QTL (F = 19; ENFP = 0.02) was detected for marbling score at centimorgan (cM) 54 on chromosome 2. Suggestive QTL were detected for fat yield at 50 cM, for retail product yield at 53 cM, and for USDA yield grade at 63 cM on chromosome 1, for marbling score at 56 cM, for retail product yield at 70 cM, and for estimated kidney, heart and pelvic fat at 79 cM on chromosome 3, for marbling score at 44 cM, for hot carcass weight at 49 cM, and for estimated kidney, heart and pelvic fat at 62 cM on chromosome 16, and for fat yield at 35 cM on chromosome 17. Two suggestive QTL for birth weight were identified, one at 12 cM on chromosome 20 and the other at 56 cM on chromosome 21. An additional suggestive QTL was detected for retail product yield, for fat yield, and for USDA yield grade at 26 cM on chromosome 26. Results presented here represent the initial search for quantitative trait loci in this family. Validation of detected QTL in other populations will be necessary.

Agriculture↗

PubMed1996

Mapping quantitative trait loci for complex binary diseases using line crosses.

A composite interval gene mapping procedure for complex binary disease traits is proposed in this paper. The binary trait of interest is assumed to be controlled by an underlying liability that is normally distributed. The liability is treated as a typical quantitative character and thus described by the usual quantitative genetics model. Translation from the liability into a binary (disease) phenotype is through the physiological threshold model. Logistic regression analysis is employed to estimate the effects and locations of putative quantitative trait loci (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). Simulation studies show that properties of this mapping procedure mimic those of the composite interval mapping for normally distributed data. Potential utilization of the QTL mapping procedure for resolving alternative genetic models (e.g., single- or two-trait-locus model) is discussed.

Chromosome Mapping↗

PubMed2001

A genomewide search for quantitative-trait loci underlying asthma.

A genomewide screen for quantitative-trait loci (QTLs) that underlie asthma was performed on 533 Chinese families with asthma, by the unified Haseman-Elston method. Nine asthma-related phenotypes were studied, including forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), airway responsiveness as indicated by methacholine (MTCH)-challenge test, serum total immunoglobulin E (TIgE), serum-specific immunoglobulin E, eosinophil count in peripheral blood, and skin-prick tests with three different allergens (cockroach, Dermatophagoides pteronyssinus, and D. farinae). Our study showed significant linkage between airway responsiveness to MTCH and D2S1780 on chromosome 2 (P<.00002) and provided suggestive evidence (P<.002) for six additional possible QTLs: D10S1435 and D22S685, for FEV1; D16S412, for FVC; D19S433, for airway responsiveness to MTCH; D1S518, for TIgE; and D4S1647, for skin reactivity to cockroach. No significant or suggestive evidence of linkage for the other four traits was found.

Adult↗

PubMed2002

Investigating the probability of sign inconsistency in the regression coefficients of markers flanking quantitative trait loci.

Estimates of the locations and effects of quantitative trait loci (QTL) can be obtained by regressing phenotype on marker genotype. Under certain basic conditions, the signs of regression coefficients flanking QTL must be the same. There is no guarantee, however, that the signs of the regression coefficient estimates will be the same. We use sign inconsistency to describe the situation in which there is disagreement between the signs of the estimated regression coefficients flanking QTL. The presence of sign inconsistency can undermine the effectiveness of QTL mapping strategies that presume intervals whose markers have regression coefficient estimates of differing sign to be devoid of QTL. This article investigates the likelihood of sign inconsistency under various conditions. We derive an analytic expression for the approximate probability of sign inconsistency in the single-QTL case. We also examine sign inconsistency probabilities when multiple QTL are present through simulation. We have discovered that the probability of sign inconsistency can be unacceptably high, even when the conditions for QTL detection are otherwise quite favorable.

Computer Simulation↗

PubMed2006

Models and partition of variance for quantitative trait loci with epistasis and linkage disequilibrium.

BACKGROUND: A genetic model about quantitative trait loci (QTL) provides a basis to interpret the genetic basis of quantitative traits in a study population, such as additive, dominance and epistatic effects of QTL and the partition of genetic variance. The standard quantitative genetics model is based on the least squares partition of genetic effects and also genetic variance in an equilibrium population. However, over years many specialized QTL models have also been proposed for applications in some specific populations. How are these models related? How to analyze and partition a QTL model and genetic variance when both epistasis and linkage disequilibrium are considered? RESULTS: Starting from the classical description of Cockerham genetic model, we first represent the model in a multiple regression setting by using indicator variables to describe the segregation of QTL alleles. In this setting, the definition of additive, dominance and epistatic effects of QTL and the basis for the partition of genetic variance are elaborated. We then build the connection between this general genetic model and a few specialized models (a haploid model, a diploid F2 model and a general two-allele model), and derive the genetic effects and partition of genetic variance for multiple QTL with epistasis and linkage disequilibrium for these specialized models. CONCLUSION: In this paper, we study extensively the composition and property of the genetic model parameters, such as genetic effects and partition of genetic variance, when both epistasis and linkage disequilibrium are considered. This is the first time that both epistasis and linkage disequilibrium are considered in modeling multiple QTL. This analysis would help us to understand the structure of genetic parameters and relationship of various genetic quantities, such as allelic frequencies and linkage disequilibrium, on the definition of genetic effects, and will also help us to understand and properly interpret estimates of the genetic effects and variance components in a QTL mapping experiment.

Algorithms↗

PubMed1997

Advances in statistical methods to map quantitative trait loci in outbred populations.

Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLs on a chromosome as an unknown.

Bayes Theorem↗

PubMed1995

Alcohol acceptance, preference, and sensitivity in mice. II. Quantitative trait loci mapping analysis using BXD recombinant inbred strains.

Quantitative trait loci (QTL) mapping of complex phenotypes has emerged as an important feature of the recombinant inbred (RI) strain methodology. In this second study of our series on alcohol-related behaviors in mice, we examine alcohol acceptance, preference, and hypnotic dose sensitivity (HDS) to a standard dose of alcohol measured in BXD RI strains to identify candidate QTL regions responsible for their heritability. We detected highly significant marker associations for acceptance on chromosome 12 (Eif4e), for preference on chromosome 1 (D1Rti2) and chromosome 7 (D7Mit7), and for HDS on chromosome 7 (Mpmv1). These are the strongest QTL associations that we detected, but several other candidate QTL regions are reported. Given the limited number of BXD RI strains available, the large number of markers used herein, and the consequent chance of identifying false marker associations, these RI QTL mapping results must be seen as tentative, but an important first step toward identifying QTL for alcohol-related behaviors.

Alcohol Drinking↗

PubMed2006

Quantitative trait loci associated with parasitic infection in Scottish blackface sheep.

This study aimed to identify quantitative trait loci associated with endoparasitic infection in Scottish Blackface sheep. Data were collected from 789 animals over a 3-year period. All of the animals were continually exposed to a mixed nematode infection by grazing. Faecal samples were collected in August, September and October each year at ca. 16, 20 and 24 weeks of age; Nematodirus spp. eggs were counted separately from the other species of nematodes. Blood samples were collected in October from which immunoglobulin A (IgA) activity was measured and DNA was extracted for genotyping. In total, 139 Microsatellite markers were genotyped across eight chromosomal regions (chromosomes 1, 2, 3, 5, 14, 18, 20 and 21) in the sires and progeny were genotyped for the markers that were polymorphic in their sire. Evidence was found for quantitative trait loci (QTL) on chromosomes 2, 3, 14 and 20. QTL associated with specific IgA activity were identified in chromosomes 3 and 20, in regions close to IFNG (chromosome 3) and the MHC (chromosome 20). QTL associated with Nematodirus FEC were identified on chromosomes 2, 3 and 14. Lastly, QTL associated with non-Nematodirus Strongyle FEC were identified on chromosomes 3 and 20. This study has shown that some aspects of host resistance to gastrointestinal parasites are under strong genetic control, therefore these QTL could be utilised in a marker-assisted selection scheme to increase host resistance to gastrointestinal parasites.

Animals↗

PubMed1995

Major quantitative trait loci affecting honey bee foraging behavior.

We identified two genomic regions that affect the amount of pollen stored in honey bee colonies and influence whether foragers will collect pollen or nectar. We selected for the amount of pollen stored in combs of honey bee colonies, a colony-level trait, and then used random amplified polymorphic DNA (RAPD) markers and interval mapping procedures with data from backcross colonies to identify two quantitative trait loci (pln1 and pln2, LOD 3.1 and 2.3, respectively). Quantitative trait loci effects were confirmed in a separate cross by demonstrating the cosegregation of marker alleles with the foraging behavior of individual workers. Both pln1 and pln2 had an effect on the amount of pollen carried by foragers returning to the colony, as inferred by the association between linked RAPD marker alleles, D8-.3f and 301-.55, and the individual pollen load weights of returning foragers. The alleles of the two marker loci were nonrandomly distributed with respect to foraging task. The two loci appeared to have different effects on foraging behavior. Individuals with alternative alleles for the marker linked to pln2 (but not pln1) differed with respect to the nectar sugar concentration of their nectar loads.

Animals↗

PubMed2005

Modeling quantitative trait Loci and interpretation of models.

A quantitative genetic model relates the genotypic value of an individual to the alleles at the loci that contribute to the variation in a population in terms of additive, dominance, and epistatic effects. This partition of genetic effects is related to the partition of genetic variance. A number of models have been proposed to describe this relationship: some are based on the orthogonal partition of genetic variance in an equilibrium population. We compare a few representative models and discuss their utility and potential problems for analyzing quantitative trait loci (QTL) in a segregating population. An orthogonal model implies that estimates of the genetic effects are consistent in a full or reduced model in an equilibrium population and are directly related to the partition of the genetic variance in the population. Linkage disequilibrium does not affect the estimation of genetic effects in a full model, but would in a reduced model. Certainly linkage disequilibrium would complicate the detection of QTL and epistasis. Using different models does not influence the detection of QTL and epistasis. However, it does influence the estimation and interpretation of genetic effects.

Gene Frequency↗

PubMed2004

Mapping quantitative trait loci with censored observations.

The existing statistical methods for mapping quantitative trait loci (QTL) assume that the phenotype follows a normal distribution and is fully observed. These assumptions may not be satisfied when the phenotype pertains to the survival time or failure time, which has a skewed distribution and is usually subject to censoring due to random loss of follow-up or limited duration of the experiment. In this article, we propose an interval-mapping approach for censored failure time phenotypes. We formulate the effects of QTL on the failure time through parametric proportional hazards models and develop efficient likelihood-based inference procedures. In addition, we show how to assess genome-wide statistical significance. The performance of the proposed methods is evaluated through extensive simulation studies. An application to a mouse cross is provided.

Chromosome Mapping↗

PubMed2005

Methodology of mapping quantitative trait loci for ordinal traits of disease resistance in livestock.

Methodology of QTL mapping for ordinal traits of disease resistance based on the framework of a generalized linear model (GLM) was presented. The location and effect parameters of putative QTL were estimated using maximum likelihood method. The efficiency and power were compared with the linear model (LM). The factors influencing QTL detection efficiency (e.g. QTL effect and heritability) were simulated in our study too. Daughter design with multiple families was applied,and the number of segregating population was 500. Results showed that the threshold model has a certain advantage in location estimation and power of QTL mapping, and has efficiency and accuracy for ordinal traits. In addition,the accuracy of QTL mapping depends on the effect of putative quantitative trait loci and the value of heritability. With the increase of QTL effect and heritability, the accuracy of QTL mapping improves slightly.

Animals↗

PubMed2004

Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle.

We report putative quantitative trait loci affecting female fertility and milk production traits using the merged data from two research groups that conducted independent genome scans in Dairy Bull DNA Repository grandsire families to identify quantitative trait loci (QTL) affecting economically important traits. Six families used by both groups had been genotyped for 367 microsatellite markers covering 2713.5 cM of the cattle genome (90%), with an average spacing of 7.4 cM. Phenotypic traits included PTA for pregnancy rate and daughter deviations for milk, protein and fat yields, protein and fat percentages, somatic cell score, and productive life. Analysis of the merged dataset identified putative quantitative trait loci that were not detected in the separate studies, and the pregnancy rate PTA estimates that recently became available allowed detection of pregnancy rate QTL for the first time. Sixty-one putative significant marker effects were identified within families, and 13 were identified across families. Highly significant effects were found on chromosome 3 affecting fat percentage and protein yield, on chromosome 6 affecting protein and fat percentages, on chromosome 14 affecting fat percentage, on chromosome 18 affecting pregnancy rate, and on chromosome 20 affecting protein percentage. Within-family analysis detected putative QTL associated with pregnancy rate on six chromosomes, with the effect on chromosome 18 being the most significant statistically. These findings may help identify the most useful markers available for QTL detection and, eventually, for marker-assisted selection for improvement of these economically important traits.

Animals↗

PubMed2004

Bayesian point estimation of quantitative trait loci.

In this article, we consider the problem of the estimation of quantitative trait loci (QTL), those chromosomal regions at which genetic information affecting some quantitative trait is encoded. Generally the number of such encoding sites is unknown, and associations between neutral molecular marker genotypes and observed trait phenotypes are sought to locate them. We consider a Bayesian model for simple experimental designs, and discuss the existing approaches to inference for this problem. In particular, we focus on locating positions of the best candidate markers segregating for the trait, a situation which is of primary interest in comparative mapping. We introduce a loss function for estimating both the number of QTL and their location, and we illustrate its application via simulated and real data.

Animals↗

PubMed1999

Allele dose analysis in recombinant inbred strains: a tool for multiple phenotype analysis with implications for quantitative trait loci mapping.

A considerable investment in genetic mapping is necessary to confirm linkages of quantitative trait loci for complex traits such as ethanol sensitivity, a significant predictor of alcoholism. Before embarking on such intensive mapping efforts in large intercrosses, we suggest an approach based on genetic marker data in recombinant strains that yield a rationale for selecting a battery of related phenotypes for confirmation studies of quantitative trait loci action. Using this approach with selected strains of mice, we retrospectively consider the relationship between ethanol sensitivity and neurotensin levels in several mammalian brain regions.

Alcoholism↗

PubMed2000

Mining the quantitative trait loci associated with oral glucose tolerance in the OLETF rat.

Although the synergetic effects of multiple marker loci regarding quantitative traits such as blood glucose level have attracted interest, previous conclusions have been based on assumptions that each marker locus behaves independently of the other, leading to approximation. To cope with this problem, this paper focuses on the effects of multiple genetic factors and tries to find significant marker combinations by using conjunctive rules regarding genotypes at multiple marker loci. Application of the proposed method on the OLETF model rat of non-insulin dependent diabetes mellitus (NIDDM) has found significant combinations of marker loci with respect to oral glucose tolerance (OGT).

Animals↗

PubMed1997

Novel quantitative trait loci for blood pressure and related traits on rat chromosomes 1, 10, and 18.

Hypertension and diabetes mellitus are known to be frequently associated. The genetic dissection of diseases such as hypertension or diabetes mellitus is possible by using experimental crosses, which allow identification of loci influencing phenotypic traits (quantitative trait loci - QTLs). In this study the spontaneously hypertensive rat (SHR) and spontaneously diabetic, but normotensive rat (BB/OK) were crossed and the F2 population was analysed in order to search for QTLs on selected chromosomes (1, 10, 18) for blood pressure and some metabolic traits related to diabetes, renal function and hypertension. There were 3 regions found on chromosome 1 which showed linkage to blood pressure. The strongest evidence for linkage was observed between loci Igf2 and D1Mgh12. On chromosome 10 there was a QTL for blood pressure found between Ppy and Abp and on chromosome 18 there were three regions (Ttr-Grl, Tilp-Gja1, Olf-D18Mit9) with linkage to blood pressure. Since the 24 hr albumin and phosphate excretion correlated with blood pressure in F2 hybrids, the same regions were linked to both parameters. Region with linkage to serum concentrations of cholesterol (probably located beyond the terminal marker Ttr of the linkage group) were also found. The results of this study with a new F2(BB x SHR) population confirm the existence of previously described blood pressure loci (Sa and Bp2) and showed novel QTLs on chromosomes 1, 10 and 18.

Albuminuria↗